{
  "abstract": "Quantum processing of classical data fundamentally relies on efficiently mapping classical datasets onto the amplitudes of quantum states. Preparing large amplitude-encoded states, however, remains a major bottleneck in quantum computing. In this paper, we introduce a novel state-preparation framework we call Q-MERGE, that addresses this bottleneck by partitioning a large target state into $M$ $n$-qubit segments, preparing these segments independently and in parallel, and then coherently combining them into a single amplitude-encoded state using SELECT-SWAP operations and measurement. Q-MERGE is agnostic to the segment-level preparation method, allowing existing techniques to be applied to smaller subproblems while providing a tunable trade-off between circuit depth and qubit count. With mid-circuit measurement and preparation-register reuse, the required ancilla qubits can be reduced from O(Mn) to O(n). Applied to a real-world $32 x 1024$ ultrasound dataset, Q-MERGE achieves an infidelity of $3.5 x 10^{-9}$, compared with $6.2 x 10^{-3}$ for direct preparation using the same underlying method, a more than six orders of magnitude improvement at equal CNOT circuit depth. We demonstrate its feasibility experimentally with a 53 qubit circuit comprised of 954 1-qubit gates and 478 2-qubit gates, on the Quantinuum System Model H2 trapped-ion quantum computer. The prepared state is validated using shadow-overlap tomography. Numerical analysis of Haar-random states indicates consistent success probability for up to $M=10^7$ segments, supporting the scalability of Q-MERGE for encoding massive classical datasets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.04247",
  "author_total": 3,
  "authors": [
    {
      "name": "Archie Butterworth"
    },
    {
      "name": "Jens Renders"
    },
    {
      "name": "Jingbo Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9nxwv0f57t8zs.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.04247",
  "fulltext_url": null,
  "id": "9nxwv0f57t8zs",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.04247",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7220665008",
  "pdf_url": "https://arxiv.org/pdf/2610.04247",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9nxwv0f57t8zs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:22Z",
      "native_id": "2610.04247",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.04247"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Q-MERGE: Parallelising Quantum State Preparation for Large-Scale Classical Data",
  "title_text": "Q-MERGE: Parallelising Quantum State Preparation for Large-Scale Classical Data",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:44Z",
  "url": "https://research.draconitic.com/paper/9nxwv0f57t8zs",
  "venue": "arXiv",
  "versions": []
}